RAPID: Rich and Accurate Auxiliary Databases for Supporting Virus Data Efforts

RAPID：丰富、准确的辅助数据库，支持病毒数据工作

• 批准号: 2029556
• 负责人: Michael Cafarella
• 金额: $ 16.48万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029556&HistoricalAwards=false
• 关键词: RAPID; Rich; Accurate; Auxiliary; Databases

This COIVD-19 RAPID project will assist in the mitigation of the negative impacts of COVID-19 on public health, society, and the economy, by creating high-quality databases from highly distributed data about medical and governmental services related to COVID-19. The project will develop software tools to help in the creation of "auxiliary" databases with high-quality data to assist in making better decisions, avoiding fraud, and yielding high-quality analysis sooner in the urgent and rapidly evolving situation created by the coronavirus pandemic. The techniques that will be used to achieve high-quality include:(1) linking "background data" to the data sets to enable quality-checking and fraud detection. For example, ensuring that hospital information listed in the medical resource database is annotated with an accurate phone number so that a volunteer can contact the hospital and check on the accuracy of the data, and (2) creating new "join keys" to enable easy integration of data in the auxiliary database with other data. The project will work closely with other related COVID-19 RAPID efforts which are working on various aspects of data and information collection from the Web.The project will focus on creating two high-quality databases using these strategies: (1) A unified medical institution auxiliary database, which will be a database of all known US medical institutions and (2) A unified government office auxiliary database, which will be a database of all known government offices in the United States—city halls, courts, licensing offices, etc.—at any level of government. Both these data sets are crucial for ensuring that citizens receive a base level of medical aid and government assistance. These resources would be beneficial not only for this particular pandemic, but would become essential resources, in general, for the future. The proposed auxiliary data set creation infrastructure will include a rich schema of background information, used for quality-checking, and a set of join keys for data integration. While there is a huge array of medical institution data sets online, many of the data sets are misaligned due to lack of standard names and/or data integration keys since different projects make different local decisions in choosing these values that may not be universally compatible. As a result, the background information becomes less rich and makes integration with data from other institutions or analysis pipelines much more difficult. The strategies used to create this infrastructure would include: (1) synthesis of preliminary auxiliary datasets, which includes generating common, candidate attributes for all objects in the input set, for example, creating a helipad field for hospitals based on examining all hospital data in Wikidata; (2) identification of inputs with missing values, and filling in those values with a combination of Web extraction tasks and crowdsourcing tasks, and (3) flagging values that are suspected of being incorrect by, for example, automatically creating a set of machine-learned predictors for each column in the auxiliary data. The system could then run the predictor and identify outlier values.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act, and is associated with the Convergence Accelerator Track A: Open Knowledge Network.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

COIVD-19 RAPID项目将通过从高度分散的与COVID-19相关的医疗和政府服务数据创建高质量数据库，帮助减轻COVID-19对公共卫生、社会和经济的负面影响。该项目将开发软件工具，以帮助创建具有高质量数据的“辅助”数据库，以帮助在冠状病毒大流行造成的紧急和快速变化的情况下更快地做出更好的决策，避免欺诈和产生高质量的分析。将用于实现高质量的技术包括：（1）将“背景数据”与数据集联系起来，以便能够进行质量检查和欺诈检测。例如，确保医疗资源数据库中列出的医院信息用准确的电话号码注释，使得志愿者可以联系医院并检查数据的准确性，以及（2）创建新的“联接密钥”以使得能够容易地将辅助数据库中的数据与其他数据集成。该项目将与其他相关的COVID-19 RAPID工作密切合作，这些工作正在从网络收集数据和信息的各个方面。该项目将专注于使用以下策略创建两个高质量的数据库：（1）统一的医疗机构辅助数据库，这将是所有已知的美国医疗机构的数据库，以及（2）统一的政府办公室辅助数据库，这将是一个美国所有已知政府机构的数据库-市政霍尔斯、法院、许可证办公室等-任何一级政府。这两组数据对于确保公民获得基本水平的医疗援助和政府援助至关重要。这些资源不仅对这一特定流行病有益，而且将成为未来的基本资源。拟议的辅助数据集创建基础设施将包括一个丰富的背景信息模式，用于质量检查，以及一组用于数据集成的连接键。虽然在线上有大量的医疗机构数据集，但由于缺乏标准名称和/或数据集成密钥，许多数据集是不一致的，因为不同的项目在选择这些可能不通用的值时会做出不同的本地决策。因此，背景信息变得不那么丰富，并且使得与来自其他机构或分析管道的数据的集成变得更加困难。用于创建该基础设施的策略包括：（1）初步辅助数据集的合成，包括为输入集中的所有对象生成共同的候选属性，例如，基于检查维基数据中的所有医院数据为医院创建直升机停机坪字段;（2）识别具有缺失值的输入，并使用Web提取任务和众包任务的组合来填充这些值，以及（3）通过例如为辅助数据中的每一列自动创建一组机器学习的预测器来标记被怀疑为不正确的值。该RAPID奖项由综合活动办公室的融合加速器计划使用冠状病毒援助，救济和经济安全（CARES）法案的资金颁发，并与融合加速器轨道A相关联：开放知识网络。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。